Autoregressive decoders in large language models (LLMs) excel at capturing users’ sequential behaviors for generative recommendations. However, they inherently struggle to leverage graph-structured user-item interactions, which are widely recognized as beneficial. This paper presents AGRec, adapting LLMs’ decoders with graph reasoning for recommendation. We reveal that LLMs and graph neural networks (GNNs) manifest complementary strengths in distinct user domains. Building on this, we augment the decoding logits of LLMs with an auxiliary GNN model to optimize token generation. Moreover, we introduce a rankable finite state machine to tackle two challenges: (1) adjusting autoregressive generation with discriminative decoders that directly predict user-item similarity, and (2) token homogeneity, where LLMs often generate items with similar prefix tokens, narrowing the scope of beam search. This approach offers a novel perspective to enhance LLMs with graph knowledge. Our AGRec outperforms state-of-the-art models in sequential recommendations. Our code is available online.

The multi-label aspect category detection (MACD) task has attracted great attention in sentiment analysis. Many recent methods have formulated the MACD task by learning robust prototypes to represent categories with limited support samples. However, few of them address the noise categories in the support set that hinder their models from effective prototype generations. To this end, we propose a causal denoising prototypical network (CDPN) for few-shot MACD. We reveal the underlying relation between causal inference and contrastive learning, and present causal contrastive learning (CCL) using discrete and continuous noise as negative samples. We empirically found that CCL can (1) prevent models from overly predicting more categories and (2) mitigate semantic ambiguity issues among categories. Experimental results show that CDPN outperforms competitive baselines. Our code is available online.

Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generation tasks. However, existing approaches either disregard or ineffectively model the user-item high-order interactions. To this end, this paper presents an enhanced LLM-based recommender (ELMRec). We enhance whole-word embeddings to substantially enhance LLMs’ interpretation of graph-constructed interactions for recommendations, without requiring graph pre-training. This finding may inspire endeavors to incorporate rich knowledge graphs into LLM-based recommenders via whole-word embedding. We also found that LLMs often recommend items based on users’ earlier interactions rather than recent ones, and present a reranking solution. Our ELMRec outperforms state-of-the-art (SOTA) methods, especially achieving a 124.3% to 293.7% improvement over SOTA LLM-based methods in direct recommendations. Our code is available online.